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This track of the course covers the topic "Hashing".

In details, this track will cover:

  • Basics: What exactly is the concept of hashing.
  • Types: We'll look at Separate chaining and Open addressing.
  • Implementation: How to use hashing in CPP and Java.

Objective: The objective of this track is to familiarize the learners with Hashing.

Track Content:

  • 5 Video Lectures on Hashing.
  • Theoretical Articles.
  • Progrraming practice problems.
  • 10 Quiz questions for practice.

Assessment: All Tracks in every week are associated with weekly contests.

We have combined Classroom and Theory tab and created a new Learn tab for easy access. You can access Classroom and Theory from the left panel.
Classroom Theory

  Introduction to Hashing 


This is an introductory video to Hashing which takes O(1) average time to search, insert or delete elements.

  Hashing Application 


This video discusses various applications of Hashing.

  Direct Address Table 


This video starts discussing the concept of Hashing, beginning with the discussion of Direct Address Table.
Code:

  Hashing Functions 


This video discusses the working and various examples of Hash Functions.

  Collision Handling 


This video discusses the problem of Collision that one can face in Hashing and various techniques on how to handle the collision problems.

  Chaining 


This video talks about the first method of collision handling called Chaining.

  Implementation of Chaining 


Through this video, we will be learning how to implement chaining in case of occurrence of a collision.

C++ Code : https://ide.geeksforgeeks.org/WYbEzJo6Op

Java Code :  https://ide.geeksforgeeks.org/KDAC6BGUrn

  Open Addressing 


This video discusses Open Addressing which is another way of handling Collision in Hashing.

  Double Hashing 


This video introduces us to the concept of double hashing where two hash functions are used to perform the hashing operation.

  Implementation of Open Addressing 


This video discusses the implementation of Open Addressing and how can it lead to avoiding of collision in Hashing.
Codes:

  Chaining vs Open Addressing 


This video discusses the point of difference between Chaining vs Open Addressing.

  Unordered_set in C++ STL 


This video discusses the Unordered set in CPP STL along with various functions involved in the unordered_set like:

  • insert()
  • begin()
  • end()
  • size()
  • clear()
  • find()
Code:

  Unordered_map in C++ STL 


This video discusses the unordered_map in C++ STL and how can it be implemented.
Code:

  HashSet in Java 


This video discusses the HashSet in Java along with few related library functions like,

  • add()
  • contains()
  • iterator()
  • size()
  • remove()
  • isEmpty()
  • clear()

Codes:

  HashMap in Java 


This video discusses the HashMap in Java

Codes:

  Count Distinct Elements 


Code links for the problem discussed in video Cpp: Cpp code
Java: Java code

  Frequencies of array elements 


Code links of the problem discussed in video Cpp: Cpp code
Java: Java code

  Intersection of two arrays 


Code links of the problem discussed in video Cpp: Cpp code
Java: Java code

  Union of two unsorted arrays 


Code links of the problem in discussed in video Cpp: Cpp code
Java: Java code

  Pair with given sum in unsorted array 


Code links of the problem discussed in video CPP: CPP code
Java: Java code

  Subarray with zero sum 


Code links of the problem discussed in video CPP: CPP code
Java: Java code

  Subarray with given sum 


This problem is an extension of the previously discussed problem of creating a subarray with zero-sum. This problem discusses the creation of similar subarray with a given input sum.
Codes:

  Longest subarray with given sum 


Code links of the problem discussed in video CPP: CPP code
Java: Java code

  Longest Subarray with equal number of 0s and 1s 


CPP: CPP code
Java: Java code

  Longest common span with same sum in binary array 


Code links of the problem discussed in video Cpp: Cpp code
Java: Java code

  Longest Consecutive Subsequence 


Given an array, we need to find the longest subsequence that has consecutive elements. These consecutive elements may appear in any order in the subsequence.
Codes:

  Count Distinct Elements In Every Window 


Given an array, one needs to Count Distinct Elements In Every Window of Size K.
Codes:

  More than n/k Occurences 

  More than n/k Occurences (O(nk) solution) 


More than n/k Occurences (O( nk) solution)
Codes:

  Hashing - Inroduction 

Hashing is a method of storing and retrieving data from a database efficiently.

Suppose that we want to design a system for storing employee records keyed using phone numbers. And we want the following queries to be performed efficiently:
  1. Insert a phone number and the corresponding information.
  2. Search a phone number and fetch the information.
  3. Delete a phone number and the related information.
We can think of using the following data structures to maintain information about different phone numbers.
  1. An array of phone numbers and records.
  2. A linked list of phone numbers and records.
  3. A balanced binary search tree with phone numbers as keys.
  4. A direct access table.
For arrays and linked lists, we need to search in a linear fashion, which can be costly in practice. If we use arrays and keep the data sorted, then a phone number can be searched in O(Logn) time using Binary Search, but insert and delete operations become costly as we have to maintain sorted order.

With a balanced binary search tree, we get a moderate search, insert and delete time. All of these operations can be guaranteed to be in O(Logn) time.

Another solution that one can think of is to use a direct access table where we make a big array and use phone numbers as indexes in the array. An entry in the array is NIL if the phone number is not present, else the array entry stores pointer to records corresponding to the phone number. Time complexity wise this solution is the best of all, we can do all operations in O(1) time. For example, to insert a phone number, we create a record with details of the given phone number, use the phone number as an index and store the pointer to the record created in the table.
This solution has many practical limitations. The first problem with this solution is that the extra space required is huge. For example, if the phone number is of n digits, we need O(m * 10n) space for the table where m is the size of a pointer to the record. Another problem is an integer in a programming language may not store n digits.

Due to the above limitations, the Direct Access Table cannot always be used. Hashing is the solution that can be used in almost all such situations and performs extremely well as compared to above data structures like Array, Linked List, Balanced BST in practice. With hashing, we get O(1) search time on average (under reasonable assumptions) and O(n) in the worst case.

Hashing is an improvement over Direct Access Table. The idea is to use a hash function that converts a given phone number or any other key to a smaller number and uses the small number as an index in a table called a hash table.

Hash Function: A function that converts a given big phone number to a small practical integer value. The mapped integer value is used as an index in the hash table. In simple terms, a hash function maps a big number or string to a small integer that can be used as an index in the hash table.
A good hash function should have following properties:
  1. It should be efficiently computable.
  2. It should uniformly distribute the keys (Each table position be equally likely for each key).

For example, for phone numbers, a bad hash function is to take the first three digits. A better function will consider the last three digits. Please note that this may not be the best hash function. There may be better ways.

Hash Table: An array that stores pointers to records corresponding to a given phone number. An entry in hash table is NIL if no existing phone number has hash function value equal to the index for the entry.

Collision Handling: Since a hash function gets us a small number for a big key, there is a possibility that two keys result in the same value. The situation where a newly inserted key maps to an already occupied slot in the hash table is called collision and must be handled using some collision handling technique. Following are the ways to handle collisions:
  • Chaining:The idea is to make each cell of the hash table point to a linked list of records that have the same hash function value. Chaining is simple, but it requires additional memory outside the table.
  • Open Addressing: In open addressing, all elements are stored in the hash table itself. Each table entry contains either a record or NIL. When searching for an element, we one by one examine the table slots until the desired element is found or it is clear that the element is not present in the table.

  Open Addressing 

Open Addressing: Like separate chaining, open addressing is a method for handling collisions. In Open Addressing, all elements are stored in the hash table itself. So at any point, the size of the table must be greater than or equal to the total number of keys (Note that we can increase table size by copying old data if needed).

Important Operations:
  • Insert(k): Keep probing until an empty slot is found. Once an empty slot is found, insert k.
  • Search(k): Keep probing until the slot's key doesn't become equal to k or an empty slot is reached.
  • Delete(k): Delete operation is interesting. If we simply delete a key, then the search may fail. So slots of the deleted keys are marked specially as "deleted".

Insert can insert an item in a deleted slot, but the search doesn't stop at a deleted slot.

Open Addressing is done in the following ways:
  1. Linear Probing: In linear probing, we linearly probe for the next slot. For example, the typical gap between the two probes is 1 as taken in the below example also.
    let hash(x) be the slot index computed using a hash function and S be the table size.
    If slot hash(x) % S is full, then we try (hash(x) + 1) % S
    If (hash(x) + 1) % S is also full, then we try (hash(x) + 2) % S
    If (hash(x) + 2) % S is also full, then we try (hash(x) + 3) % S 
    ..................................................
    ..................................................
    Let us consider a simple hash function as “key mod 7” and a sequence of keys as 50, 700, 76, 85, 92, 73, 101.

    openAddressing
    Clustering: The main problem with linear probing is clustering, many consecutive elements form groups and it starts taking time to find a free slot or to search an element.
  2. Quadratic Probing We look for i2'th slot in i'th iteration.
    let hash(x) be the slot index computed using hash function.
    If slot hash(x) % S is full, then we try (hash(x) + 1*1) % S
    If (hash(x) + 1*1) % S is also full, then we try (hash(x) + 2*2) % S
    If (hash(x) + 2*2) % S is also full, then we try (hash(x) + 3*3) % S
    ..................................................
    ..................................................
  3. Double Hashing We use another hash function hash2(x) and look for i*hash2(x) slot in i'th rotation.
    let hash(x) be the slot index computed using hash function.
    If slot hash(x) % S is full, then we try (hash(x) + 1*hash2(x)) % S
    If (hash(x) + 1*hash2(x)) % S is also full, then we try (hash(x) + 2*hash2(x)) % S
    If (hash(x) + 2*hash2(x)) % S is also full, then we try (hash(x) + 3*hash2(x)) % S
    ..................................................
    ..................................................

    See this for step by step diagrams.

Comparison of above three:
  • Linear probing has the best cache performance but it suffers from clustering. One more advantage of Linear probing that it is easy to compute.
  • Quadratic probing lies between the two in terms of cache performance and clustering.
  • Double hashing has poor cache performance but no clustering. Double hashing requires more computation time as two hash functions need to be computed.
S.No. Seperate Chaining Open Addressing
1. Chaining is Simpler to implement. Open Addressing requires more computation.
2. In chaining, Hash table never fills up, we can always add more elements to chain. In open addressing, table may become full.
3. Chaining is Less sensitive to the hash function or load factors. Open addressing requires extra care for to avoid clustering and load factor.
4. Chaining is mostly used when it is unknown how many and how frequently keys may be inserted or deleted. Open addressing is used when the frequency and number of keys is known.
5. Cache performance of chaining is not good as keys are stored using linked list. Open addressing provides better cache performance as everything is stored in the same table.
6. Wastage of Space (Some Parts of hash table in chaining are never used). In Open addressing, a slot can be used even if an input doesn't map to it.
7. Chaining uses extra space for links. No links in Open addressing


Performance of Open Addressing: Like Chaining, the performance of hashing can be evaluated under the assumption that each key is equally likely to be hashed to any slot of the table (simple uniform hashing).

 m = Number of slots in the hash table
 n = Number of keys to be inserted in the hash table
 
 Load factor α = n/m  ( < 1 )

 Expected time to search/insert/delete < 1/(1 - α) 

 So Search, Insert and Delete take (1/(1 - α)) time

  Hashing in C++ using STL 

Hashing in C++ can be implemented using different containers present in STL as per the requirement. Usually, STL offers the below-mentioned containers for implementing hashing:
  • set
  • unordered_set
  • map
  • unordered_map

Let us take a look at each of these containers in details:

set

Sets are a type of associative containers in which each element has to be unique, because the value of the element identifies it. The value of the element cannot be modified once it is added to the set, though it is possible to remove and add the modified value of that element.

Sets are used in the situation where it is needed to check if an element is present in a list or not. It can also be done with the help of arrays, but it would take up a lot of space. Sets can also be used to solve many problems related to sorting as the elements in the set are arranged in a sorted order.

Some basic functions associated with Set:
  • begin() – Returns an iterator to the first element in the set.
  • end() – Returns an iterator to the theoretical element that follows last element in the set.
  • size() – Returns the number of elements in the set.
  • insert(val) – Inserts a new element val in the Set.
  • find(val) - Returns an iterator pointing to the element val in the set if it is present.
  • empty() – Returns whether the set is empty.

Implementation:
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
Output:
The elements in the set are: 
10 20 30 40 50 60 

50 is present

unordered_set

The unordered_set container is implemented using a hash table where keys are hashed into indices of this hash table so it is not possible to maintain any order. All operation on unordered_set takes constant time O(1) on an average which can go up to linear time in the worst case which depends on the internally used hash function but practically they perform very well and generally provide constant time search operation.

The unordered-set can contain key of any type – predefined or user-defined data structure but when we define key of a user-defined type, we need to specify our comparison function according to which keys will be compared.

set vs unordered_set

  • Set is an ordered sequence of unique keys whereas unordered_set is a set in which key can be stored in any order, so unordered.
  • Set is implemented as a balanced tree structure that is why it is possible to maintain order between the elements (by specific tree traversal). The time complexity of set operations is O(Log n) while for unordered_set, it is O(1).

Note: Like set containers, the Unordered_set also allows only unique keys.

Implementation:
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
The elements in the unordered_set are: 
10 50 30 60 40 20 

50 is present

Map container

As a set, the Map container is also associative and stores elements in an ordered way but Maps store elements in a mapped fashion. Each element has a key value and a mapped value. No two mapped values can have the same key values.

Some basic functions associated with Map:
  • begin() – Returns an iterator to the first element in the map.
  • end() – Returns an iterator to the theoretical element that follows last element in the map.
  • size() – Returns the number of elements in the map.
  • empty() – Returns whether the map is empty.
  • insert({keyvalue, mapvalue}) – Adds a new element to the map.
  • erase(iterator position) – Removes the element at the position pointed by the iterator
  • erase(const g)– Removes the key value ‘g’ from the map.
  • clear() – Removes all the elements from the map.

Implementation:
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
Output:
The map mp is : 
KEY    ELEMENT
1    40
2    30
3    60
4    20
5    50
6    50
7    10

unordered_map Container

The unordered_map is an associated container that stores elements formed by a combination of key value and a mapped value. The key value is used to uniquely identify the element and mapped value is the content associated with the key. Both key and value can be of any type predefined or user-defined.
Internally unordered_map is implemented using Hash Table, the key provided to map are hashed into indices of a hash table that is why the performance of data structure depends on hash function a lot but on an average, the cost of search, insert and delete from hash table is O(1).

Implementation:
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
Output:
The map umap is : 
KEY        ELEMENT
Contribute    30
GeeksforGeeks    10
Practice    20

unordered_map vs unordered_set


In unordered_set, we have only key, no value, these are mainly used to see presence/absence in a set. For example, consider the problem of counting frequencies of individual words. We can’t use unordered_set (or set) as we can’t store counts.

unordered_map vs map

map (like set) is an ordered sequence of unique keys whereas in the unordered_map key can be stored in any order, so unordered.
A map is implemented as a balanced tree structure that is why it is possible to maintain order between the elements (by specific tree traversal). The time complexity of map operations is O(Log n) while for unordered_set, it is O(1) on average.

  Implementing Hashing in Java 

Java provides many built-in classes and interfaces to implement hashing easily. That is, without creating any HashTable or hash function. Java mainly provides us with the following classes to implement Hashing:
  1. HashTable (A synchronized implementation of hashing): This class implements a hash table, which maps keys to values. Any non-null object can be used as a key or as a value.

    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

    Output: 
    {15=A computer, 3=Portal, 12=forGeeks, 1=Geeks}
  2. HashMap (A non-synchronized faster implementation of hashing): HashMap is also similar to HashTables in Java but it is faster in comparison as it is not synchronized. HashMap is used to store key-value pairs or to map a given value to a given key. The general application of HashMap is to count frequencies of elements present in an array or a list.

    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

    Output: 
    {34=1, 3=1, 5=2, 10=3}
  3. LinkedHashMap (Similar to HashMap, but keeps order of elements):
    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

    Output: 
    {one=practice.geeksforgeeks.org, two=code.geeksforgeeks.org, four=quiz.geeksforgeeks.org}
    Getting value for key 'one': practice.geeksforgeeks.org
    Size of the map: 3
    Is map empty? false
    Contains key 'two'? true
    Contains value 'practice.geeksforgeeks.org'? true
    delete element 'one': practice.geeksforgeeks.org
    {two=code.geeksforgeeks.org, four=quiz.geeksforgeeks.org}
  4. HashSet (Similar to HashMap, but maintains only keys, not pair): The HashSet class implements the Set interface, backed by a hash table which is actually a HashMap instance. The class also offers constant time performance for the basic operations like add, remove, contains, and size assuming that the hash function disperses the elements properly among the buckets. HashSet is generally used to keep a check on whether an element is present in a list or not.

    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

    Output: 
    [South Africa, Australia, India]

    HashSet contains India or not:true

    List after removing Australia:[South Africa, India]

    Iterating over list:
    South Africa
    India
  5. LinkedHashSet (Similar to LinkedHashMap, but maintains only keys, not pair):

    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

    Output: 
    Size of LinkedHashSet = 5
    Original LinkedHashSet:[A, B, C, D, E]
    Removing D from LinkedHashSet: true
    Trying to Remove Z which is not present: false
    Checking if A is present=true
    Updated LinkedHashSet: [A, B, C, E]
  6. TreeSet (Implements the SortedSet interface, Objects are stored in a sorted and ascending order):
    XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
    Output: 
    TreeSet: [A, B, C]

    TreeSet contains A or not:true

    TreeSet after removing A:[B, C]

    Iterating over TreeSet:
    B
    C
Separate chaining in Hashing
Submissions: 4745   Accuracy:

58.89%

  Difficulty: Easy   Marks: 2
Linear Probing in Hashing
Submissions: 9974   Accuracy:

40.64%

  Difficulty: Easy   Marks: 2
Quadratic Probing in Hashing
Submissions: 4684   Accuracy:

53.68%

  Difficulty: Easy   Marks: 2
Count Non-Repeated Elements
Submissions: 7462   Accuracy:

64.22%

  Difficulty: Easy   Marks: 2
Print Non-Repeated Elements
Submissions: 12039   Accuracy:

49.26%

  Difficulty: Easy   Marks: 2
First Repeating Element

Asked In: Amazon Oracle

Submissions: 20001   Accuracy:

48.62%

  Difficulty: Easy   Marks: 2
Intersection of two arrays

Asked In: Accolite Amazon PayPal Rockstand Microsoft

Submissions: 12556   Accuracy:

45.86%

  Difficulty: Easy   Marks: 2
Union of two arrays

Asked In: Rockstand Zoho

Submissions: 14993   Accuracy:

52.81%

  Difficulty: Basic   Marks: 1
Hashing for pair - 1
Submissions: 6969   Accuracy:

63.85%

  Difficulty: Easy   Marks: 2
Hashing for pair - 2
Submissions: 5638   Accuracy:

72.23%

  Difficulty: Easy   Marks: 2
Check if two arrays are equal or not

Asked In: Goldman Sachs

Submissions: 12713   Accuracy:

50%

  Difficulty: Basic   Marks: 1
Numbers containing 1, 2 and 3

Asked In: Goldman Sachs

Submissions: 2183   Accuracy:

40.39%

  Difficulty: Easy   Marks: 2
Subarray with 0 sum

Asked In: Paytm

Submissions: 25844   Accuracy:

49.91%

  Difficulty: Easy   Marks: 2
Winner of an election

Asked In: Adobe

Submissions: 11745   Accuracy:

49.75%

  Difficulty: Easy   Marks: 2
Subarray range with given sum
Submissions: 8271   Accuracy:

38.98%

  Difficulty: Easy   Marks: 2
Positive Negative Pair

Asked In: Amazon

Submissions: 4044   Accuracy:

34.69%

  Difficulty: Easy   Marks: 2
Zero Sum Subarrays

Asked In: Amazon Microsoft OYO Rooms

Submissions: 8852   Accuracy:

50.74%

  Difficulty: Medium   Marks: 4
Subarrays with equal 1s and 0s
Submissions: 12104   Accuracy:

50.04%

  Difficulty: Medium   Marks: 4
Sort an array according to the other

Asked In: Amazon Microsoft

Submissions: 5720   Accuracy:

54.65%

  Difficulty: Medium   Marks: 4
Sorting Elements of an Array by Frequency

Asked In: Oracle Zycus

Submissions: 18337   Accuracy:

47.44%

  Difficulty: Medium   Marks: 4
Question 1 [1 Marks]
How many different insertion sequences of the key values using the hash function h(k) = k mod 10 and linear probing will result in the hash table shown below?

A
10
B
20
C
30
D
40
Explanation

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